## Nombre de participants se déclarant comme joueurs : 29
## Nombre de femmes se déclarant comme joueuses : 3
## Age médian des joueurs : 15
## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers: 5"
## [1] "Total number of outliers motor task: 2"
## [1] "Total number of outliers perceptive task: 1"
## [1] "Total number of outliers logical task: 2"
## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, a6dfu5ljd, dyg7cga2o, tmxmxmwhi, zp9bc59o5"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers: 5"
## [1] "Total number of outliers motor task: 0"
## [1] "Total number of outliers perceptive task: 5"
## [1] "Total number of outliers logical task: 0"
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1953.7 1975.3 -972.8 1945.7 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1396 -0.7500 0.2888 0.7385 2.8481
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5631 0.7504
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0298 0.1873 -5.499 3.83e-08 ***
## difficulty 2.9618 0.2146 13.803 < 2e-16 ***
## timeNorm -0.5280 0.2020 -2.614 0.00895 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.539
## timeNorm -0.571 -0.009
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
##
## Logique2 Motrice Sensoriel
## 0 1624 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.050110
## 1st Qu.:-0.438217
## Median :-0.118832
## Mean :-0.002364
## 3rd Qu.: 0.296005
## Max. : 1.658440
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"
## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1261.1 1282.7 -626.5 1253.1 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3943 -0.3586 0.1131 0.3536 6.6338
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7241 0.8509
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.3288 0.2583 -12.885 <2e-16 ***
## difficulty 8.2778 0.4068 20.346 <2e-16 ***
## timeNorm -0.2933 0.2674 -1.097 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.650
## timeNorm -0.519 -0.046
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
##
## Logique2 Motrice Sensoriel
## 0 0 1624
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.6765404
## 1st Qu.:-0.4435738
## Median : 0.0778425
## Mean :-0.0007671
## 3rd Qu.: 0.4353921
## Max. : 1.5192471
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"
## 0% 25% 50% 75% 100%
## -1.51924712 -0.43539206 -0.07784249 0.44357377 1.67654045
## 0% 25% 50% 75% 100%
## -1.51924712 -0.43539206 -0.07784249 0.44357377 1.67654045
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1426.5 1447.8 -709.2 1418.5 1504
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9435 -0.5021 -0.1156 0.5089 4.9862
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.577 1.256
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8650 0.2652 -7.033 2.01e-12 ***
## difficulty 5.6686 0.3206 17.680 < 2e-16 ***
## timeNorm -1.9313 0.2573 -7.507 6.04e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.496
## timeNorm -0.378 -0.227
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
##
## Logique2 Motrice Sensoriel
## 1508 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.7902825
## 1st Qu.:-0.7784485
## Median :-0.3355504
## Mean :-0.0003123
## 3rd Qu.: 0.7369882
## Max. : 3.1275697
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"
## 0% 25% 50% 75% 100%
## -3.1275697 -0.7369882 0.3355504 0.7784485 1.7902825
## 0% 25% 50% 75% 100%
## -3.1275697 -0.7369882 0.3355504 0.7784485 1.7902825
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1442117
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.07199342
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03301126
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08913015
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.05061255
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.08596507
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3393258
##
## [1] "self.eff.on.level.s 0.34 0.013 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 26 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07281435
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1554335
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2101231
##
## [1] "risk.av.on.level.s 0.21 0.034 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1347244
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09369113
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2137687
##
## [1] "age.on.level.s 0.21 0.027 *"
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1275074
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2377395
##
## [1] "sexe.on.level.m -0.24 0.032 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.008649769
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03108211
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.82775747 -0.05457213
## sample estimates:
## difference in location
## -0.4558716
##
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4361429 0.4780691
## sample estimates:
## difference in location
## -0.01100307
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8271570 0.5994594
## sample estimates:
## difference in location
## -0.04046848
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.51384, p-value = 0.6074
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03114828
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -3.5194, p-value = 0.0004325
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2108941
##
## [1] "pbg.on.error -0.21 0.00043 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.4336, p-value = 0.1517
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07585348
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.74916, p-value = 0.4538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06883117
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.43819, p-value = 0.6613
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04025974
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.94693, p-value = 0.3437
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.09049774
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 3.9311, p-value = 8.455e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.253602
##
## [1] "sexe.on.error 0.25 8.5e-05 ***"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.9825, p-value = 0.04743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2202014
##
## [1] "sexe.on.error.m 0.22 0.047 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.3795, p-value = 0.01734
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2642985
##
## [1] "sexe.on.error.s 0.26 0.017 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4235, p-value = 0.01537
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.279739
##
## [1] "sexe.on.error.l 0.28 0.015 *"
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 4126, p-value = 8.517e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.05397826 0.12809100
## sample estimates:
## difference in location
## 0.09264717
##
## [1] "sexe.on.error.2 0.093 8.5e-05 *** mean(A): -0.11 mean(B): -0.01"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 455, p-value = 0.04774
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.0005518641 0.1439064370
## sample estimates:
## difference in location
## 0.07761885
##
## [1] "sexe.on.error.m.2 0.078 0.048 * mean(A): -0.097 mean(B): -0.012"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 489, p-value = 0.01678
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.01781275 0.15896562
## sample estimates:
## difference in location
## 0.09669579
##
## [1] "sexe.on.error.s.2 0.097 0.017 * mean(A): -0.11 mean(B): -0.004"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 432, p-value = 0.01476
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.02021314 0.16115025
## sample estimates:
## difference in location
## 0.1018427
##
## [1] "sexe.on.error.l.2 0.1 0.015 * mean(A): -0.12 mean(B): -0.016"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.91097, p-value = 0.3623
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05234983
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.21777, p-value = 0.8276
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02158799
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.15983, p-value = 0.873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.01583119
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2413, p-value = 0.2145
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.12803
## Warning: Removed 82 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.8685, p-value = 0.004124
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2249577
##
## [1] "self.eff.on.error -0.22 0.0041 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.686, p-value = 0.09179
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2294667
##
## [1] "self.eff.on.error -0.23 0.092 ."
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3708, p-value = 0.1704
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1873078
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 26 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.7973, p-value = 0.07228
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2564331
##
## [1] "self.eff.on.error -0.26 0.072 ."
## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.00054 49 0.84 :(
## 2: 0.09375 0.02500 57 0.21 :(
## 3: 0.15625 -0.01300 57 0.33 :(
## 4: 0.21875 0.02400 57 0.46 :(
## 5: 0.28125 -0.01900 58 0.56 :(
## 6: 0.34375 0.00150 57 0.94 :(
## 7: 0.40625 0.00450 56 0.82 :(
## 8: 0.46875 -0.01600 58 0.65 :(
## 9: 0.53125 0.00450 56 0.84 :(
## 10: 0.59375 0.01100 58 0.78 :(
## 11: 0.65625 -0.05500 58 0.065 .
## 12: 0.71875 -0.10000 58 0.00011 ***
## 13: 0.78125 -0.15000 57 7.8e-08 ***
## 14: 0.84375 -0.18000 55 4.5e-08 ***
## 15: 0.90625 -0.20000 57 4.9e-11 ***
## 16: 0.96875 -0.17000 57 4.9e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 49 0.84 :(
## 2: 57 0.21 :(
## 3: 57 0.33 :(
## 4: 57 0.46 :(
## 5: 58 0.56 :(
## 6: 57 0.94 :(
## 7: 56 0.82 :(
## 8: 58 0.65 :(
## 9: 56 0.84 :(
## 10: 58 0.78 :(
## 11: 58 0.065 .
## 12: 58 0.00011 ***
## 13: 57 7.8e-08 ***
## 14: 55 4.5e-08 ***
## 15: 57 4.9e-11 ***
## 16: 57 4.9e-11 ***
## [1] 56.6
## [1] 2.19
## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0044 30 0.93 :(
## 2: 0.09375 0.0045 32 0.61 :(
## 3: 0.15625 -0.0130 40 0.39 :(
## 4: 0.21875 -0.0044 40 0.88 :(
## 5: 0.28125 -0.0074 36 0.88 :(
## 6: 0.34375 0.0370 36 0.43 :(
## 7: 0.40625 0.0400 39 0.36 :(
## 8: 0.46875 0.0670 36 0.32 :(
## 9: 0.53125 0.0470 36 0.24 :(
## 10: 0.59375 0.0130 39 0.52 :(
## 11: 0.65625 -0.0130 34 0.69 :(
## 12: 0.71875 -0.1300 35 0.002 **
## 13: 0.78125 -0.1500 35 0.0018 **
## 14: 0.84375 -0.1800 22 3e-04 ***
## 15: 0.90625 -0.1900 22 3.9e-05 ***
## 16: 0.96875 -0.1500 11 0.0037 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 30 0.93 :(
## 2: 32 0.61 :(
## 3: 40 0.39 :(
## 4: 40 0.88 :(
## 5: 36 0.88 :(
## 6: 36 0.43 :(
## 7: 39 0.36 :(
## 8: 36 0.32 :(
## 9: 36 0.24 :(
## 10: 39 0.52 :(
## 11: 34 0.69 :(
## 12: 35 0.002 **
## 13: 35 0.0018 **
## 14: 22 3e-04 ***
## 15: 22 3.9e-05 ***
## 16: 11 0.0037 **
## [1] 32.7
## [1] 7.96
## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 29 0.18 :(
## 2: 0.09375 0.0130 33 0.36 :(
## 3: 0.15625 0.0220 31 0.61 :(
## 4: 0.21875 -0.0045 35 0.78 :(
## 5: 0.28125 -0.0670 34 0.12 :(
## 6: 0.34375 -0.0820 37 0.11 :(
## 7: 0.40625 -0.0620 36 0.05 .
## 8: 0.46875 -0.1300 37 0.018 *
## 9: 0.53125 0.0220 34 0.68 :(
## 10: 0.59375 -0.0018 37 1 :(
## 11: 0.65625 -0.0970 36 0.026 *
## 12: 0.71875 -0.0900 37 0.041 *
## 13: 0.78125 -0.1300 38 0.00075 ***
## 14: 0.84375 -0.1800 36 5.6e-05 ***
## 15: 0.90625 -0.2300 37 1.1e-07 ***
## 16: 0.96875 -0.2100 34 3.6e-07 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 29 0.18 :(
## 2: 33 0.36 :(
## 3: 31 0.61 :(
## 4: 35 0.78 :(
## 5: 34 0.12 :(
## 6: 37 0.11 :(
## 7: 36 0.05 .
## 8: 37 0.018 *
## 9: 34 0.68 :(
## 10: 37 1 :(
## 11: 36 0.026 *
## 12: 37 0.041 *
## 13: 38 0.00075 ***
## 14: 36 5.6e-05 ***
## 15: 37 1.1e-07 ***
## 16: 34 3.6e-07 ***
## [1] 35.1
## [1] 2.46
## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 9 0.53 :(
## 2: 0.09375 -0.0220 19 0.76 :(
## 3: 0.15625 -0.0700 18 0.21 :(
## 4: 0.21875 -0.0045 17 0.78 :(
## 5: 0.28125 0.0400 18 0.69 :(
## 6: 0.34375 0.0850 16 0.42 :(
## 7: 0.40625 0.1200 20 0.049 *
## 8: 0.46875 0.0760 19 0.61 :(
## 9: 0.53125 -0.1000 18 0.3 :(
## 10: 0.59375 -0.1100 18 0.19 :(
## 11: 0.65625 -0.0850 22 0.27 :(
## 12: 0.71875 -0.0760 21 0.055 .
## 13: 0.78125 -0.1000 20 0.019 *
## 14: 0.84375 -0.1400 26 0.0053 **
## 15: 0.90625 -0.1500 26 7.6e-06 ***
## 16: 0.96875 -0.1600 26 8.5e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.53 :(
## 2: 19 0.76 :(
## 3: 18 0.21 :(
## 4: 17 0.78 :(
## 5: 18 0.69 :(
## 6: 16 0.42 :(
## 7: 20 0.049 *
## 8: 19 0.61 :(
## 9: 18 0.3 :(
## 10: 18 0.19 :(
## 11: 22 0.27 :(
## 12: 21 0.055 .
## 13: 20 0.019 *
## 14: 26 0.0053 **
## 15: 26 7.6e-06 ***
## 16: 26 8.5e-06 ***
## [1] 19.6
## [1] 4.27
## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.094 8 0.21 :(
## 3: 0.15625 -0.099 26 0.015 *
## 4: 0.21875 -0.076 40 0.0065 **
## 5: 0.28125 -0.067 45 0.055 .
## 6: 0.34375 -0.058 47 0.21 :(
## 7: 0.40625 -0.013 49 0.8 :(
## 8: 0.46875 0.031 49 0.73 :(
## 9: 0.53125 0.076 51 0.15 :(
## 10: 0.59375 0.025 51 0.55 :(
## 11: 0.65625 -0.013 53 0.45 :(
## 12: 0.71875 -0.052 51 0.079 .
## 13: 0.78125 -0.067 44 0.029 *
## 14: 0.84375 -0.094 27 0.0073 **
## 15: 0.90625 -0.078 14 0.00076 ***
## 16: 0.96875 -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.21 :(
## 2: 26 0.015 *
## 3: 40 0.0065 **
## 4: 45 0.055 .
## 5: 47 0.21 :(
## 6: 49 0.8 :(
## 7: 49 0.73 :(
## 8: 51 0.15 :(
## 9: 51 0.55 :(
## 10: 53 0.45 :(
## 11: 51 0.079 .
## 12: 44 0.029 *
## 13: 27 0.0073 **
## 14: 14 0.00076 ***
## 15: 6 0.034 *
## [1] 37.4
## [1] 16.7
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 -0.0940 8 0.21 :(
## 3: 0.15625 -0.1200 24 0.005 **
## 4: 0.21875 -0.0760 26 0.031 *
## 5: 0.28125 -0.0670 25 0.12 :(
## 6: 0.34375 0.0130 26 0.8 :(
## 7: 0.40625 0.0320 25 0.67 :(
## 8: 0.46875 0.0880 24 0.14 :(
## 9: 0.53125 0.0760 23 0.21 :(
## 10: 0.59375 0.0970 24 0.038 *
## 11: 0.65625 0.0081 25 0.94 :(
## 12: 0.71875 -0.0470 22 0.078 .
## 13: 0.78125 -0.1000 15 0.26 :(
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.21 :(
## 2: 24 0.005 **
## 3: 26 0.031 *
## 4: 25 0.12 :(
## 5: 26 0.8 :(
## 6: 25 0.67 :(
## 7: 24 0.14 :(
## 8: 23 0.21 :(
## 9: 24 0.038 *
## 10: 25 0.94 :(
## 11: 22 0.078 .
## 12: 15 0.26 :(
## [1] 22.2
## [1] 5.36
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.2000 2 1 :(
## 4: 0.21875 -0.2200 14 0.15 :(
## 5: 0.28125 -0.0990 20 0.38 :(
## 6: 0.34375 -0.1600 20 0.08 .
## 7: 0.40625 -0.0490 22 0.31 :(
## 8: 0.46875 -0.0160 21 0.63 :(
## 9: 0.53125 0.1400 21 0.0048 **
## 10: 0.59375 0.0130 21 0.86 :(
## 11: 0.65625 -0.0130 21 0.94 :(
## 12: 0.71875 0.0430 22 0.43 :(
## 13: 0.78125 -0.0099 21 0.75 :(
## 14: 0.84375 -0.0940 19 0.017 *
## 15: 0.90625 NA 6 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 14 0.15 :(
## 3: 20 0.38 :(
## 4: 20 0.08 .
## 5: 22 0.31 :(
## 6: 21 0.63 :(
## 7: 21 0.0048 **
## 8: 21 0.86 :(
## 9: 21 0.94 :(
## 10: 22 0.43 :(
## 11: 21 0.75 :(
## 12: 19 0.017 *
## [1] 18.7
## [1] 5.66
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 -0.049 2 1 :(
## 8: 0.46875 -0.180 4 0.58 :(
## 9: 0.53125 -0.400 7 0.071 .
## 10: 0.59375 -0.290 6 0.14 :(
## 11: 0.65625 -0.230 7 0.16 :(
## 12: 0.71875 -0.250 7 0.047 *
## 13: 0.78125 -0.180 8 0.023 *
## 14: 0.84375 -0.110 8 0.29 :(
## 15: 0.90625 -0.110 8 0.013 *
## 16: 0.96875 -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 4 0.58 :(
## 3: 7 0.071 .
## 4: 6 0.14 :(
## 5: 7 0.16 :(
## 6: 7 0.047 *
## 7: 8 0.023 *
## 8: 8 0.29 :(
## 9: 8 0.013 *
## 10: 6 0.034 *
## [1] 6.3
## [1] 1.95
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).
## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 44 0.033 *
## 2: 0.09375 -0.094 53 0.014 *
## 3: 0.15625 -0.071 48 0.046 *
## 4: 0.21875 -0.040 40 0.21 :(
## 5: 0.28125 -0.067 38 0.42 :(
## 6: 0.34375 -0.058 36 0.21 :(
## 7: 0.40625 -0.049 37 0.53 :(
## 8: 0.46875 -0.110 37 0.033 *
## 9: 0.53125 -0.140 30 0.027 *
## 10: 0.59375 -0.170 33 0.029 *
## 11: 0.65625 -0.085 34 0.029 *
## 12: 0.71875 -0.150 34 0.0034 **
## 13: 0.78125 -0.210 38 0.00063 ***
## 14: 0.84375 -0.150 45 8.4e-05 ***
## 15: 0.90625 -0.170 53 1.7e-10 ***
## 16: 0.96875 -0.140 56 6.3e-11 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.033 *
## 2: 53 0.014 *
## 3: 48 0.046 *
## 4: 40 0.21 :(
## 5: 38 0.42 :(
## 6: 36 0.21 :(
## 7: 37 0.53 :(
## 8: 37 0.033 *
## 9: 30 0.027 *
## 10: 33 0.029 *
## 11: 34 0.029 *
## 12: 34 0.0034 **
## 13: 38 0.00063 ***
## 14: 45 8.4e-05 ***
## 15: 53 1.7e-10 ***
## 16: 56 6.3e-11 ***
## [1] 41
## [1] 7.94
## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 11 0.82 :(
## 2: 0.09375 -0.094 10 0.084 .
## 3: 0.15625 -0.085 11 0.38 :(
## 4: 0.21875 -0.076 7 0.27 :(
## 5: 0.28125 0.063 11 0.62 :(
## 6: 0.34375 -0.240 7 0.021 *
## 7: 0.40625 -0.190 7 0.2 :(
## 8: 0.46875 -0.170 7 0.15 :(
## 9: 0.53125 -0.100 6 0.53 :(
## 10: 0.59375 -0.210 7 0.11 :(
## 11: 0.65625 -0.230 6 0.29 :(
## 12: 0.71875 -0.430 6 0.036 *
## 13: 0.78125 -0.320 7 0.078 .
## 14: 0.84375 -0.130 8 0.11 :(
## 15: 0.90625 -0.120 10 0.0053 **
## 16: 0.96875 -0.150 11 0.0035 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.82 :(
## 2: 10 0.084 .
## 3: 11 0.38 :(
## 4: 7 0.27 :(
## 5: 11 0.62 :(
## 6: 7 0.021 *
## 7: 7 0.2 :(
## 8: 7 0.15 :(
## 9: 6 0.53 :(
## 10: 7 0.11 :(
## 11: 6 0.29 :(
## 12: 6 0.036 *
## 13: 7 0.078 .
## 14: 8 0.11 :(
## 15: 10 0.0053 **
## 16: 11 0.0035 **
## [1] 8.25
## [1] 2.02
## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 24 0.012 *
## 2: 0.09375 -0.094 25 0.011 *
## 3: 0.15625 -0.085 21 0.076 .
## 4: 0.21875 -0.076 18 0.16 :(
## 5: 0.28125 -0.140 14 0.52 :(
## 6: 0.34375 -0.022 17 0.96 :(
## 7: 0.40625 -0.120 17 0.046 *
## 8: 0.46875 -0.180 19 0.015 *
## 9: 0.53125 -0.250 15 0.031 *
## 10: 0.59375 -0.170 17 0.071 .
## 11: 0.65625 -0.190 15 0.028 *
## 12: 0.71875 -0.220 13 0.017 *
## 13: 0.78125 -0.210 20 0.0023 **
## 14: 0.84375 -0.240 19 0.0013 **
## 15: 0.90625 -0.220 25 1.2e-05 ***
## 16: 0.96875 -0.150 25 1.2e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 24 0.012 *
## 2: 25 0.011 *
## 3: 21 0.076 .
## 4: 18 0.16 :(
## 5: 14 0.52 :(
## 6: 17 0.96 :(
## 7: 17 0.046 *
## 8: 19 0.015 *
## 9: 15 0.031 *
## 10: 17 0.071 .
## 11: 15 0.028 *
## 12: 13 0.017 *
## 13: 20 0.0023 **
## 14: 19 0.0013 **
## 15: 25 1.2e-05 ***
## 16: 25 1.2e-05 ***
## [1] 19
## [1] 4.03
## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 9 0.53 :(
## 2: 0.09375 -0.0220 18 0.89 :(
## 3: 0.15625 -0.0130 16 0.38 :(
## 4: 0.21875 0.0190 15 0.8 :(
## 5: 0.28125 -0.1000 13 0.48 :(
## 6: 0.34375 -0.0220 12 0.91 :(
## 7: 0.40625 0.1700 13 0.091 .
## 8: 0.46875 0.1000 11 0.5 :(
## 9: 0.53125 -0.0310 9 0.63 :(
## 10: 0.59375 -0.0220 9 0.91 :(
## 11: 0.65625 -0.0130 13 0.78 :(
## 12: 0.71875 -0.0045 15 0.67 :(
## 13: 0.78125 -0.0063 11 1 :(
## 14: 0.84375 -0.0580 18 0.18 :(
## 15: 0.90625 -0.1400 18 0.00016 ***
## 16: 0.96875 -0.1200 20 9.2e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.53 :(
## 2: 18 0.89 :(
## 3: 16 0.38 :(
## 4: 15 0.8 :(
## 5: 13 0.48 :(
## 6: 12 0.91 :(
## 7: 13 0.091 .
## 8: 11 0.5 :(
## 9: 9 0.63 :(
## 10: 9 0.91 :(
## 11: 13 0.78 :(
## 12: 15 0.67 :(
## 13: 11 1 :(
## 14: 18 0.18 :(
## 15: 18 0.00016 ***
## 16: 20 9.2e-05 ***
## [1] 13.8
## [1] 3.55
## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0045 35 0.79 :(
## 2: 0.09375 0.1100 40 0.012 *
## 3: 0.15625 0.1100 40 0.097 .
## 4: 0.21875 0.1600 42 0.0092 **
## 5: 0.28125 0.1500 34 0.051 .
## 6: 0.34375 0.0850 39 0.21 :(
## 7: 0.40625 0.0220 44 0.18 :(
## 8: 0.46875 -0.0045 39 0.93 :(
## 9: 0.53125 -0.0310 37 0.71 :(
## 10: 0.59375 -0.0220 41 0.61 :(
## 11: 0.65625 -0.0490 39 0.42 :(
## 12: 0.71875 -0.1500 38 0.0068 **
## 13: 0.78125 -0.1700 43 0.00035 ***
## 14: 0.84375 -0.2400 41 1.8e-07 ***
## 15: 0.90625 -0.2800 40 3.6e-08 ***
## 16: 0.96875 -0.3300 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 35 0.79 :(
## 2: 40 0.012 *
## 3: 40 0.097 .
## 4: 42 0.0092 **
## 5: 34 0.051 .
## 6: 39 0.21 :(
## 7: 44 0.18 :(
## 8: 39 0.93 :(
## 9: 37 0.71 :(
## 10: 41 0.61 :(
## 11: 39 0.42 :(
## 12: 38 0.0068 **
## 13: 43 0.00035 ***
## 14: 41 1.8e-07 ***
## 15: 40 3.6e-08 ***
## 16: 25 1.3e-05 ***
## [1] 38.6
## [1] 4.47
## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0044 26 0.9 :(
## 2: 0.09375 0.0370 26 0.24 :(
## 3: 0.15625 0.0940 24 0.14 :(
## 4: 0.21875 0.1600 24 0.036 *
## 5: 0.28125 0.1100 17 0.32 :(
## 6: 0.34375 0.0850 21 0.24 :(
## 7: 0.40625 0.0940 22 0.25 :(
## 8: 0.46875 0.0670 20 0.44 :(
## 9: 0.53125 0.0400 18 0.46 :(
## 10: 0.59375 -0.0220 21 0.42 :(
## 11: 0.65625 -0.0130 17 0.57 :(
## 12: 0.71875 -0.1500 18 0.097 .
## 13: 0.78125 -0.1400 21 0.026 *
## 14: 0.84375 -0.2000 18 0.00057 ***
## 15: 0.90625 -0.2600 15 0.00071 ***
## 16: 0.96875 NA 1 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 26 0.9 :(
## 2: 26 0.24 :(
## 3: 24 0.14 :(
## 4: 24 0.036 *
## 5: 17 0.32 :(
## 6: 21 0.24 :(
## 7: 22 0.25 :(
## 8: 20 0.44 :(
## 9: 18 0.46 :(
## 10: 21 0.42 :(
## 11: 17 0.57 :(
## 12: 18 0.097 .
## 13: 21 0.026 *
## 14: 18 0.00057 ***
## 15: 15 0.00071 ***
## [1] 20.5
## [1] 3.4
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.064 9 0.9 :(
## 2: 0.09375 0.310 13 0.014 *
## 3: 0.15625 0.240 13 0.16 :(
## 4: 0.21875 0.210 15 0.056 .
## 5: 0.28125 0.076 11 0.31 :(
## 6: 0.34375 -0.033 12 1 :(
## 7: 0.40625 -0.049 15 0.75 :(
## 8: 0.46875 -0.064 12 0.36 :(
## 9: 0.53125 -0.070 11 0.45 :(
## 10: 0.59375 0.085 12 0.22 :(
## 11: 0.65625 -0.160 14 0.19 :(
## 12: 0.71875 -0.290 14 0.032 *
## 13: 0.78125 -0.160 15 0.021 *
## 14: 0.84375 -0.260 15 0.0013 **
## 15: 0.90625 -0.320 15 0.00072 ***
## 16: 0.96875 -0.340 14 0.0011 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.9 :(
## 2: 13 0.014 *
## 3: 13 0.16 :(
## 4: 15 0.056 .
## 5: 11 0.31 :(
## 6: 12 1 :(
## 7: 15 0.75 :(
## 8: 12 0.36 :(
## 9: 11 0.45 :(
## 10: 12 0.22 :(
## 11: 14 0.19 :(
## 12: 14 0.032 *
## 13: 15 0.021 *
## 14: 15 0.0013 **
## 15: 15 0.00072 ***
## 16: 14 0.0011 **
## [1] 13.1
## [1] 1.82
## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 1 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 -0.0280 3 1 :(
## 5: 0.28125 0.1500 6 0.13 :(
## 6: 0.34375 0.1600 6 0.4 :(
## 7: 0.40625 0.0460 7 0.15 :(
## 8: 0.46875 -0.0016 7 1 :(
## 9: 0.53125 -0.1000 8 0.44 :(
## 10: 0.59375 -0.1700 8 0.36 :(
## 11: 0.65625 0.1300 8 0.29 :(
## 12: 0.71875 -0.1000 6 0.67 :(
## 13: 0.78125 -0.2100 7 0.2 :(
## 14: 0.84375 -0.2700 8 0.042 *
## 15: 0.90625 -0.2600 10 0.0059 **
## 16: 0.96875 -0.3100 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 3 1 :(
## 2: 6 0.13 :(
## 3: 6 0.4 :(
## 4: 7 0.15 :(
## 5: 7 1 :(
## 6: 8 0.44 :(
## 7: 8 0.36 :(
## 8: 8 0.29 :(
## 9: 6 0.67 :(
## 10: 7 0.2 :(
## 11: 8 0.042 *
## 12: 10 0.0059 **
## 13: 10 0.0059 **
## [1] 7.23
## [1] 1.83
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.85521 -0.20000 0.03999 0.20805 0.69174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02757 0.02336 -1.180 0.2381
## timeNorm 0.03482 0.02460 1.416 0.1570
## obj.diff -0.08659 0.03066 -2.824 0.0048 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07429011)
##
## Null deviance: 121.29 on 1623 degrees of freedom
## Residual deviance: 120.42 on 1621 degrees of freedom
## AIC: 391.67
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.78610 -0.11567 0.04559 0.11403 0.81494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001581 0.016841 0.094 0.925
## timeNorm 0.009074 0.022514 0.403 0.687
## obj.diff -0.212404 0.017421 -12.192 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06402259)
##
## Null deviance: 113.31 on 1623 degrees of freedom
## Residual deviance: 103.78 on 1621 degrees of freedom
## AIC: 150.11
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70209 -0.22699 0.01746 0.22707 0.66363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.179866 0.023643 7.608 4.89e-14 ***
## timeNorm 0.007075 0.029400 0.241 0.81
## obj.diff -0.476741 0.025105 -18.990 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09502739)
##
## Null deviance: 179.81 on 1507 degrees of freedom
## Residual deviance: 143.02 on 1505 degrees of freedom
## AIC: 735.3
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5089286 0.6008109 -0.07683886 112 0.0057 **
## 2: 4.5 0.4889456 0.5714407 -0.07445527 168 8e-04 ***
## 3: 7.5 0.4863946 0.5416953 -0.04763643 168 0.023 *
## 4: 10.5 0.5008503 0.5401276 -0.03488016 168 0.13 :(
## 5: 13.5 0.4447279 0.5174551 -0.06672273 168 0.0017 **
## 6: 16.5 0.4931973 0.5305272 -0.02102698 168 0.36 :(
## 7: 19.5 0.4736395 0.5315528 -0.04770887 168 0.021 *
## 8: 22.5 0.4455782 0.4897264 -0.03529116 168 0.093 .
## 9: 25.5 0.4464286 0.4805683 -0.02474658 168 0.31 :(
## 10: 28.5 0.4166667 0.4572889 -0.03958163 168 0.083 .
## time error.diff shapes
## 1: 1.5 -0.07683886 24
## 2: 4.5 -0.07445527 24
## 3: 7.5 -0.04763643 24
## 4: 10.5 -0.03488016 16
## 5: 13.5 -0.06672273 24
## 6: 16.5 -0.02102698 16
## 7: 19.5 -0.04770887 24
## 8: 22.5 -0.03529116 16
## 9: 25.5 -0.02474658 16
## 10: 28.5 -0.03958163 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4285714 0.5941293 -0.14841500 112 7.1e-09 ***
## 2: 4.5 0.5212585 0.6104788 -0.09151470 168 4.2e-08 ***
## 3: 7.5 0.4379252 0.5299114 -0.09240609 168 1.5e-07 ***
## 4: 10.5 0.4642857 0.5824635 -0.10424800 168 1.8e-11 ***
## 5: 13.5 0.4302721 0.5656294 -0.11814246 168 2.1e-13 ***
## 6: 16.5 0.4064626 0.5333505 -0.11438030 168 4.2e-11 ***
## 7: 19.5 0.4685374 0.5641391 -0.08414982 168 1.9e-08 ***
## 8: 22.5 0.4311224 0.5656705 -0.12484954 168 2.4e-12 ***
## 9: 25.5 0.4923469 0.5874740 -0.09752555 168 7.2e-11 ***
## 10: 28.5 0.4608844 0.5711020 -0.10647805 168 1.2e-10 ***
## time error.diff shapes
## 1: 1.5 -0.14841500 24
## 2: 4.5 -0.09151470 24
## 3: 7.5 -0.09240609 24
## 4: 10.5 -0.10424800 24
## 5: 13.5 -0.11814246 24
## 6: 16.5 -0.11438030 24
## 7: 19.5 -0.08414982 24
## 8: 22.5 -0.12484954 24
## 9: 25.5 -0.09752555 24
## 10: 28.5 -0.10647805 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4354396 0.5969130 -0.146652665 104 1.6e-06 ***
## 2: 4.5 0.5027473 0.6297636 -0.123587134 156 4.8e-06 ***
## 3: 7.5 0.4972527 0.5544687 -0.063129356 156 0.013 *
## 4: 10.5 0.4908425 0.5229882 -0.045103811 156 0.074 .
## 5: 13.5 0.4734432 0.5312208 -0.047826904 156 0.091 .
## 6: 16.5 0.4661172 0.5008164 -0.043716799 156 0.089 .
## 7: 19.5 0.3937729 0.4456698 -0.053440226 156 0.047 *
## 8: 22.5 0.3864469 0.4198655 -0.032783828 156 0.22 :(
## 9: 25.5 0.3800366 0.3963862 -0.012966789 156 0.66 :(
## 10: 28.5 0.3864469 0.3637653 -0.007979433 156 0.82 :(
## time error.diff shapes
## 1: 1.5 -0.146652665 24
## 2: 4.5 -0.123587134 24
## 3: 7.5 -0.063129356 24
## 4: 10.5 -0.045103811 16
## 5: 13.5 -0.047826904 16
## 6: 16.5 -0.043716799 16
## 7: 19.5 -0.053440226 24
## 8: 22.5 -0.032783828 16
## 9: 25.5 -0.012966789 16
## 10: 28.5 -0.007979433 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.76605 -0.17974 0.09827 0.16562 0.72448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07622 0.03024 2.521 0.0119 *
## timeNorm 0.03377 0.03261 1.036 0.3005
## obj.diff -0.32572 0.03080 -10.575 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09040828)
##
## Null deviance: 109.823 on 1101 degrees of freedom
## Residual deviance: 99.359 on 1099 degrees of freedom
## AIC: 483.77
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77371 -0.18878 0.04154 0.20265 0.74395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05416 0.01987 2.726 0.00648 **
## timeNorm 0.02489 0.02395 1.039 0.29882
## obj.diff -0.28152 0.02237 -12.584 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08063932)
##
## Null deviance: 160.31 on 1826 degrees of freedom
## Residual deviance: 147.09 on 1824 degrees of freedom
## AIC: 589.84
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74927 -0.18962 -0.03788 0.19768 0.76912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05189 0.01808 2.870 0.00415 **
## timeNorm 0.03130 0.02302 1.359 0.17423
## obj.diff -0.22828 0.02353 -9.702 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07220067)
##
## Null deviance: 139.33 on 1826 degrees of freedom
## Residual deviance: 131.69 on 1824 degrees of freedom
## AIC: 387.88
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5281955 0.7253320 -0.18770872 76 8.8e-08 ***
## 2: 4.5 0.6190476 0.7689197 -0.12239921 114 4.8e-07 ***
## 3: 7.5 0.5877193 0.7094956 -0.11127265 114 1.1e-05 ***
## 4: 10.5 0.5614035 0.6993212 -0.11956311 114 4.2e-06 ***
## 5: 13.5 0.5789474 0.7269567 -0.12152900 114 8.4e-08 ***
## 6: 16.5 0.5375940 0.6678003 -0.11156080 114 4.8e-06 ***
## 7: 19.5 0.5664160 0.6813782 -0.08953959 114 0.0011 **
## 8: 22.5 0.5626566 0.7025643 -0.11945525 114 1.6e-06 ***
## 9: 25.5 0.5162907 0.6530197 -0.11174022 114 6.7e-07 ***
## 10: 28.5 0.5852130 0.6646574 -0.07927214 114 0.00029 ***
## time error.diff shapes
## 1: 1.5 -0.18770872 24
## 2: 4.5 -0.12239921 24
## 3: 7.5 -0.11127265 24
## 4: 10.5 -0.11956311 24
## 5: 13.5 -0.12152900 24
## 6: 16.5 -0.11156080 24
## 7: 19.5 -0.08953959 24
## 8: 22.5 -0.11945525 24
## 9: 25.5 -0.11174022 24
## 10: 28.5 -0.07927214 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4659864 0.6122964 -0.12648243 126 3.2e-06 ***
## 2: 4.5 0.4958428 0.6315007 -0.12319809 189 1.4e-10 ***
## 3: 7.5 0.4580499 0.5313254 -0.07335336 189 6.1e-05 ***
## 4: 10.5 0.5245654 0.5829560 -0.06767141 189 0.00041 ***
## 5: 13.5 0.4580499 0.5530913 -0.08538869 189 5.9e-05 ***
## 6: 16.5 0.4777022 0.5603358 -0.08178293 189 0.00022 ***
## 7: 19.5 0.4671202 0.5589971 -0.08014481 189 3.5e-05 ***
## 8: 22.5 0.4058957 0.5023611 -0.09726237 189 8.4e-06 ***
## 9: 25.5 0.4761905 0.5383267 -0.06695149 189 0.0048 **
## 10: 28.5 0.4331066 0.4988396 -0.07033125 189 0.00069 ***
## time error.diff shapes
## 1: 1.5 -0.12648243 24
## 2: 4.5 -0.12319809 24
## 3: 7.5 -0.07335336 24
## 4: 10.5 -0.06767141 24
## 5: 13.5 -0.08538869 24
## 6: 16.5 -0.08178293 24
## 7: 19.5 -0.08014481 24
## 8: 22.5 -0.09726237 24
## 9: 25.5 -0.06695149 24
## 10: 28.5 -0.07033125 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4081633 0.5050608 -0.08630678 126 5e-04 ***
## 2: 4.5 0.4436886 0.4751064 -0.03789574 189 0.11 :(
## 3: 7.5 0.4195011 0.4509208 -0.03026032 189 0.15 :(
## 4: 10.5 0.3998488 0.4247627 -0.03200187 189 0.14 :(
## 5: 13.5 0.3613001 0.4096366 -0.05160912 189 0.01 *
## 6: 16.5 0.3824641 0.3959053 -0.01776122 189 0.36 :(
## 7: 19.5 0.3537415 0.3718157 -0.02981185 189 0.12 :(
## 8: 22.5 0.3529856 0.3585562 -0.01201852 189 0.56 :(
## 9: 25.5 0.3605442 0.3443352 0.00846663 189 0.75 :(
## 10: 28.5 0.3129252 0.3146320 -0.02201649 189 0.27 :(
## time error.diff shapes
## 1: 1.5 -0.08630678 24
## 2: 4.5 -0.03789574 16
## 3: 7.5 -0.03026032 16
## 4: 10.5 -0.03200187 16
## 5: 13.5 -0.05160912 24
## 6: 16.5 -0.01776122 16
## 7: 19.5 -0.02981185 16
## 8: 22.5 -0.01201852 16
## 9: 25.5 0.00846663 16
## 10: 28.5 -0.02201649 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77782 -0.16141 0.07773 0.18154 0.65784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.462332 0.119085 -3.882 0.000135 ***
## timeNorm 0.004994 0.071725 0.070 0.944555
## obj.diff 0.309216 0.135862 2.276 0.023773 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09109535)
##
## Null deviance: 21.338 on 231 degrees of freedom
## Residual deviance: 20.861 on 229 degrees of freedom
## AIC: 107.53
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.6339286 0.8544830 -0.1813070 16 0.0013 **
## 2: 4.5 0.5773810 0.7995145 -0.1900501 24 0.00057 ***
## 3: 7.5 0.5714286 0.7551085 -0.1583598 24 0.0043 **
## 4: 10.5 0.5892857 0.7836615 -0.1770491 24 0.0011 **
## 5: 13.5 0.6071429 0.8240112 -0.1620422 24 0.0018 **
## 6: 16.5 0.4821429 0.7818411 -0.2673553 24 0.00028 ***
## 7: 19.5 0.5000000 0.7263256 -0.2097781 24 0.0096 **
## 8: 22.5 0.6130952 0.7654436 -0.1099361 24 0.11 :(
## 9: 25.5 0.5119048 0.7908307 -0.2703569 24 0.00018 ***
## 10: 28.5 0.5476190 0.7394768 -0.1501698 24 0.0087 **
## time error.diff shapes
## 1: 1.5 -0.1813070 24
## 2: 4.5 -0.1900501 24
## 3: 7.5 -0.1583598 24
## 4: 10.5 -0.1770491 24
## 5: 13.5 -0.1620422 24
## 6: 16.5 -0.2673553 24
## 7: 19.5 -0.2097781 24
## 8: 22.5 -0.1099361 16
## 9: 25.5 -0.2703569 24
## 10: 28.5 -0.1501698 24
## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81780 -0.19138 0.04321 0.17708 0.68822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.13079 0.04059 -3.222 0.00134 **
## timeNorm 0.07430 0.03785 1.963 0.05008 .
## obj.diff 0.09494 0.05455 1.740 0.08228 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06868557)
##
## Null deviance: 44.015 on 637 degrees of freedom
## Residual deviance: 43.615 on 635 degrees of freedom
## AIC: 106.86
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5227273 0.6251419 -0.087913622 44 0.062 .
## 2: 4.5 0.5432900 0.6224524 -0.067787288 66 0.053 .
## 3: 7.5 0.5064935 0.5482212 -0.033170955 66 0.34 :(
## 4: 10.5 0.5519481 0.5744464 -0.017320378 66 0.7 :(
## 5: 13.5 0.5086580 0.5455378 -0.027725556 66 0.47 :(
## 6: 16.5 0.5519481 0.5560045 0.008925402 66 0.85 :(
## 7: 19.5 0.5519481 0.5704673 -0.010678355 66 0.76 :(
## 8: 22.5 0.4307359 0.5060978 -0.079405018 66 0.035 *
## 9: 25.5 0.4870130 0.4999714 -0.012031106 66 0.76 :(
## 10: 28.5 0.4870130 0.5016324 -0.017813543 66 0.61 :(
## time error.diff shapes
## 1: 1.5 -0.087913622 16
## 2: 4.5 -0.067787288 16
## 3: 7.5 -0.033170955 16
## 4: 10.5 -0.017320378 16
## 5: 13.5 -0.027725556 16
## 6: 16.5 0.008925402 16
## 7: 19.5 -0.010678355 16
## 8: 22.5 -0.079405018 24
## 9: 25.5 -0.012031106 16
## 10: 28.5 -0.017813543 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7823 -0.1833 0.0022 0.2021 0.7030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08025 0.03159 -2.541 0.0113 *
## timeNorm 0.06361 0.03395 1.874 0.0613 .
## obj.diff 0.06975 0.04864 1.434 0.1520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06414613)
##
## Null deviance: 48.468 on 753 degrees of freedom
## Residual deviance: 48.174 on 751 degrees of freedom
## AIC: 73.823
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4587912 0.5021701 -0.029565591 52 0.5 :(
## 2: 4.5 0.4157509 0.4581003 -0.036201957 78 0.23 :(
## 3: 7.5 0.4432234 0.4705078 -0.025469870 78 0.34 :(
## 4: 10.5 0.4304029 0.4361551 -0.003198821 78 0.91 :(
## 5: 13.5 0.3406593 0.3993679 -0.061812903 78 0.028 *
## 6: 16.5 0.4468864 0.4316421 0.017600071 78 0.45 :(
## 7: 19.5 0.3992674 0.4386951 -0.038308162 78 0.2 :(
## 8: 22.5 0.4065934 0.3910376 0.012148843 78 0.56 :(
## 9: 25.5 0.3919414 0.3686849 0.027863007 78 0.37 :(
## 10: 28.5 0.3168498 0.3329405 -0.021946631 78 0.56 :(
## time error.diff shapes
## 1: 1.5 -0.029565591 16
## 2: 4.5 -0.036201957 16
## 3: 7.5 -0.025469870 16
## 4: 10.5 -0.003198821 16
## 5: 13.5 -0.061812903 24
## 6: 16.5 0.017600071 16
## 7: 19.5 -0.038308162 16
## 8: 22.5 0.012148843 16
## 9: 25.5 0.027863007 16
## 10: 28.5 -0.021946631 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.82141 -0.15374 0.03313 0.11265 0.74133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06993 0.03099 2.256 0.0244 *
## timeNorm 0.01707 0.03894 0.438 0.6612
## obj.diff -0.24589 0.03153 -7.798 2.95e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06836725)
##
## Null deviance: 43.628 on 579 degrees of freedom
## Residual deviance: 39.448 on 577 degrees of freedom
## AIC: 94.901
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4785714 0.5999008 -0.13404889 40 0.0058 **
## 2: 4.5 0.6547619 0.7009436 -0.07926068 60 0.048 *
## 3: 7.5 0.5523810 0.6171368 -0.08578639 60 0.059 .
## 4: 10.5 0.5261905 0.6349960 -0.10209314 60 0.0039 **
## 5: 13.5 0.5452381 0.6718447 -0.11959740 60 4.8e-06 ***
## 6: 16.5 0.5214286 0.5575435 -0.07312179 60 0.094 .
## 7: 19.5 0.5952381 0.6401329 -0.06573366 60 0.11 :(
## 8: 22.5 0.5833333 0.6695307 -0.11887633 60 0.0015 **
## 9: 25.5 0.4928571 0.5891457 -0.10298824 60 3.6e-06 ***
## 10: 28.5 0.5809524 0.6285546 -0.08367159 60 0.015 *
## time error.diff shapes
## 1: 1.5 -0.13404889 24
## 2: 4.5 -0.07926068 24
## 3: 7.5 -0.08578639 16
## 4: 10.5 -0.10209314 24
## 5: 13.5 -0.11959740 24
## 6: 16.5 -0.07312179 16
## 7: 19.5 -0.06573366 16
## 8: 22.5 -0.11887633 24
## 9: 25.5 -0.10298824 24
## 10: 28.5 -0.08367159 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7480 -0.1019 0.0036 0.1302 0.8064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03649 0.02404 -1.518 0.130
## timeNorm 0.02091 0.03262 0.641 0.522
## obj.diff -0.21518 0.02506 -8.586 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05998238)
##
## Null deviance: 47.750 on 724 degrees of freedom
## Residual deviance: 43.307 on 722 degrees of freedom
## AIC: 22.519
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3971429 0.5955390 -0.15959256 50 5.5e-05 ***
## 2: 4.5 0.4247619 0.5658258 -0.10782010 75 2.1e-07 ***
## 3: 7.5 0.3733333 0.4935521 -0.09904220 75 4.4e-06 ***
## 4: 10.5 0.4838095 0.6203172 -0.10663262 75 1.3e-08 ***
## 5: 13.5 0.3923810 0.5287833 -0.11227501 75 5.8e-06 ***
## 6: 16.5 0.3523810 0.5469777 -0.15730356 75 5.6e-10 ***
## 7: 19.5 0.4361905 0.5547914 -0.08851555 75 8.8e-06 ***
## 8: 22.5 0.3314286 0.5052840 -0.14842435 75 1e-07 ***
## 9: 25.5 0.5085714 0.6171176 -0.10460691 75 1.5e-05 ***
## 10: 28.5 0.4247619 0.5694054 -0.12008650 75 1.3e-07 ***
## time error.diff shapes
## 1: 1.5 -0.15959256 24
## 2: 4.5 -0.10782010 24
## 3: 7.5 -0.09904220 24
## 4: 10.5 -0.10663262 24
## 5: 13.5 -0.11227501 24
## 6: 16.5 -0.15730356 24
## 7: 19.5 -0.08851555 24
## 8: 22.5 -0.14842435 24
## 9: 25.5 -0.10460691 24
## 10: 28.5 -0.12008650 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61001 -0.11261 -0.00817 0.12283 0.83868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00753 0.03454 0.218 0.828
## timeNorm -0.03366 0.04852 -0.694 0.488
## obj.diff -0.21449 0.03736 -5.741 2.21e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0583853)
##
## Null deviance: 20.394 on 318 degrees of freedom
## Residual deviance: 18.450 on 316 degrees of freedom
## AIC: 4.0881
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4090909 0.5804316 -0.14022422 22 0.00043 ***
## 2: 4.5 0.4978355 0.5474815 -0.06265206 33 0.077 .
## 3: 7.5 0.3766234 0.4539543 -0.07335885 33 0.0058 **
## 4: 10.5 0.3073593 0.4009189 -0.10218734 33 0.0015 **
## 5: 13.5 0.3073593 0.4562521 -0.14644435 33 8.3e-05 ***
## 6: 16.5 0.3203463 0.4583925 -0.10064086 33 0.00044 ***
## 7: 19.5 0.3116883 0.4472135 -0.09931286 33 2.5e-05 ***
## 8: 22.5 0.3809524 0.5140759 -0.13924753 33 0.00019 ***
## 9: 25.5 0.4545455 0.5170628 -0.06839461 33 0.035 *
## 10: 28.5 0.3246753 0.4704987 -0.12173515 33 6e-04 ***
## time error.diff shapes
## 1: 1.5 -0.14022422 24
## 2: 4.5 -0.06265206 16
## 3: 7.5 -0.07335885 24
## 4: 10.5 -0.10218734 24
## 5: 13.5 -0.14644435 24
## 6: 16.5 -0.10064086 24
## 7: 19.5 -0.09931286 24
## 8: 22.5 -0.13924753 24
## 9: 25.5 -0.06839461 24
## 10: 28.5 -0.12173515 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6740 -0.2383 0.1913 0.2315 0.4468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31220 0.08931 3.496 0.000547 ***
## timeNorm 0.04250 0.07455 0.570 0.569082
## obj.diff -0.66919 0.08659 -7.729 1.84e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1143287)
##
## Null deviance: 40.669 on 289 degrees of freedom
## Residual deviance: 32.812 on 287 degrees of freedom
## AIC: 199.05
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5428571 0.8728737 -0.29669053 20 9.5e-06 ***
## 2: 4.5 0.5809524 0.8803963 -0.25559896 30 5.6e-05 ***
## 3: 7.5 0.6714286 0.8577229 -0.13483882 30 0.00061 ***
## 4: 10.5 0.6095238 0.7604994 -0.13331250 30 0.031 *
## 5: 13.5 0.6238095 0.7595374 -0.13478768 30 0.13 :(
## 6: 16.5 0.6142857 0.7970813 -0.14997730 30 0.0011 **
## 7: 19.5 0.5619048 0.7279108 -0.11259797 30 0.096 .
## 8: 22.5 0.4809524 0.7183280 -0.19119972 30 0.00034 ***
## 9: 25.5 0.5666667 0.6705190 -0.08796937 30 0.35 :(
## 10: 28.5 0.6238095 0.6770076 -0.05763367 30 0.33 :(
## time error.diff shapes
## 1: 1.5 -0.29669053 24
## 2: 4.5 -0.25559896 24
## 3: 7.5 -0.13483882 24
## 4: 10.5 -0.13331250 24
## 5: 13.5 -0.13478768 16
## 6: 16.5 -0.14997730 24
## 7: 19.5 -0.11259797 16
## 8: 22.5 -0.19119972 24
## 9: 25.5 -0.08796937 16
## 10: 28.5 -0.05763367 16
## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61833 -0.30332 0.04882 0.26823 0.55284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32571 0.04760 6.843 2.48e-11 ***
## timeNorm -0.07278 0.05505 -1.322 0.187
## obj.diff -0.64371 0.05013 -12.842 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1022827)
##
## Null deviance: 64.433 on 463 degrees of freedom
## Residual deviance: 47.152 on 461 degrees of freedom
## AIC: 263.84
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4955357 0.6208173 -0.102134051 32 0.054 .
## 2: 4.5 0.5416667 0.7465592 -0.219964008 48 1.4e-05 ***
## 3: 7.5 0.5238095 0.5671145 -0.057721683 48 0.21 :(
## 4: 10.5 0.5505952 0.5362800 -0.012090840 48 0.86 :(
## 5: 13.5 0.4910714 0.6014588 -0.102933948 48 0.058 .
## 6: 16.5 0.5714286 0.5871636 -0.024564724 48 0.7 :(
## 7: 19.5 0.3988095 0.5497972 -0.159240917 48 0.0053 **
## 8: 22.5 0.4880952 0.4926560 -0.007795557 48 0.89 :(
## 9: 25.5 0.4107143 0.4679547 -0.052574583 48 0.41 :(
## 10: 28.5 0.3720238 0.3847404 -0.033401276 48 0.52 :(
## time error.diff shapes
## 1: 1.5 -0.102134051 16
## 2: 4.5 -0.219964008 24
## 3: 7.5 -0.057721683 16
## 4: 10.5 -0.012090840 16
## 5: 13.5 -0.102933948 16
## 6: 16.5 -0.024564724 16
## 7: 19.5 -0.159240917 24
## 8: 22.5 -0.007795557 16
## 9: 25.5 -0.052574583 16
## 10: 28.5 -0.033401276 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71089 -0.17869 -0.08719 0.21121 0.71297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10150 0.02962 3.427 0.000643 ***
## timeNorm 0.04189 0.03875 1.081 0.280065
## obj.diff -0.32835 0.03789 -8.665 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0796471)
##
## Null deviance: 67.040 on 753 degrees of freedom
## Residual deviance: 59.815 on 751 degrees of freedom
## AIC: 237.02
##
## Number of Fisher Scoring iterations: 2
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3571429 0.4760639 -0.111779680 52 0.0053 **
## 2: 4.5 0.4487179 0.4614922 -0.022793530 78 0.58 :(
## 3: 7.5 0.4139194 0.4300504 -0.025871902 78 0.57 :(
## 4: 10.5 0.4084249 0.4234581 -0.028539435 78 0.33 :(
## 5: 13.5 0.4047619 0.4001833 0.011787281 78 0.72 :(
## 6: 16.5 0.3443223 0.3337317 -0.003294444 78 0.96 :(
## 7: 19.5 0.3260073 0.2730373 0.026983247 78 0.5 :(
## 8: 22.5 0.2875458 0.2602781 0.022005152 78 0.54 :(
## 9: 25.5 0.2893773 0.2469083 0.020133496 78 0.63 :(
## 10: 28.5 0.3040293 0.2303798 0.036118595 78 0.47 :(
## time error.diff shapes
## 1: 1.5 -0.111779680 24
## 2: 4.5 -0.022793530 16
## 3: 7.5 -0.025871902 16
## 4: 10.5 -0.028539435 16
## 5: 13.5 0.011787281 16
## 6: 16.5 -0.003294444 16
## 7: 19.5 0.026983247 16
## 8: 22.5 0.022005152 16
## 9: 25.5 0.020133496 16
## 10: 28.5 0.036118595 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.89243 -0.18839 0.03948 0.19600 0.68812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03898 0.01319 2.956 0.00316 **
## est.confidence.norm -0.19092 0.02330 -8.194 5.07e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0718048)
##
## Null deviance: 121.29 on 1623 degrees of freedom
## Residual deviance: 116.47 on 1622 degrees of freedom
## AIC: 335.41
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.87812 -0.07387 0.00530 0.08343 0.93023
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.113321 0.013285 -8.530 <2e-16 ***
## est.confidence.norm -0.002485 0.023014 -0.108 0.914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06985935)
##
## Null deviance: 113.31 on 1623 degrees of freedom
## Residual deviance: 113.31 on 1622 degrees of freedom
## AIC: 290.81
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.93929 -0.17946 -0.01235 0.18914 0.90729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.00194 0.01798 -0.108 0.91409
## est.confidence.norm -0.09542 0.03025 -3.154 0.00164 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1186153)
##
## Null deviance: 179.81 on 1507 degrees of freedom
## Residual deviance: 178.63 on 1506 degrees of freedom
## AIC: 1068.7
##
## Number of Fisher Scoring iterations: 2
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1138.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4577 -0.5580 -0.0296 0.5724 4.4769
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01651 0.1285
## Residual 0.07157 0.2675
## Number of obs: 4756, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04222 0.01899 81.00000 -2.224 0.028933 *
## est.confidence.norm -0.05669 0.01550 4745.00000 -3.657 0.000258 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.409
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -217
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5313 -0.6734 0.0668 0.6616 2.9503
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02759 0.1661
## Residual 0.04608 0.2147
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.02408 0.02741 102.60000 -0.879 0.3817
## est.confidence.norm -0.06189 0.03105 1418.90000 -1.994 0.0464 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.554
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 110.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2365 -0.4898 0.0294 0.4751 4.4098
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01218 0.1104
## Residual 0.05823 0.2413
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.14395 0.02208 144.50000 -6.519 1.11e-09 ***
## est.confidence.norm 0.05854 0.03049 961.70000 1.920 0.0552 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.693
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 700.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3346 -0.5829 -0.0444 0.5885 3.5505
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.03712 0.1927
## Residual 0.08466 0.2910
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.08864 0.03414 102.60000 -2.596 0.0108 *
## est.confidence.norm 0.07232 0.03849 1325.70000 1.879 0.0604 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.583
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